%Naive Bayes Classifier

SleepData = [avgFrequency maxFrequency continuousVar1 continuousVar2 continuousVar3 continuousVar4];
SleepStageClass % eg. = ['Awake' 'Awake' 'Movement' 'Stage1' 'Stage2' 'Stage1' 'Stage1' 'Stage2' 'Stage1' 'Stage3' 'Stage4' 'StageREM']'
Model = NaiveBayes.fit(SleepData,SleepStageClass);
%Model =
%NaiveBayes.fit(SleepData,SleepStageClass,'dist',{'normal','normal','normal','kernel','normal','kernel'});
%normal or kernel need to decide on that.

%Classify = Model.predict(SleepData);
%cMat1 = confusionmat(SleepStageClass,Classify)
